@inproceedings{tanneberg_humanoids16,

title = {Deep Spiking Networks for Model-based Planning in Humanoids},

author = {Daniel Tanneberg and Alexandros Paraschos and Jan Peters and Elmar Rueckert},

url = {https://cps.unileoben.ac.at/wp/Humanoids2016Tanneberg.pdf, Article File

https://cps.unileoben.ac.at/wp/resources/videos/humanoids_slower_540p.mp4, Supplementary Video},

year  = {2016},

date = {2016-11-16},

booktitle = {Proceedings of the International Conference on Humanoid Robots (HUMANOIDS)},

crossref = {p10980},

key = {codyco and tacman},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rueckert2015,

title = {Extracting Low-Dimensional Control Variables for Movement Primitives},

author = {Elmar Rueckert and Jan Mundo and Alexandros Paraschos and Jan Peters and Gerhard Neumann},

url = {https://cps.unileoben.ac.at/wp/ICRA2015Rueckert.pdf, Article File

https://cps.unileoben.ac.at/wp/resources/code/MATLAB_LatentManifoldPrimitives_2015Rueckert.zip, MATALB Code

https://cps.unileoben.ac.at/wp/resources/videos/KUKA_LatentManifold_ProMPs_REAL_Elmar.mp4, Supplementary Video},

year  = {2015},

date = {2015-01-01},

booktitle = {Proceedings of the International Conference on Robotics and Automation (ICRA)},

crossref = {p10796},

key = {3rdhand, codyco},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rueckert2019c,

title = {Dynamic Control Strategies for Cable-Driven Master Slave Robots},

author = {Elmar Rueckert and Philipp Jauer and Alexander Derksen and Achim Schweikard},

editor = {Tobias Keck},

doi = {10.18416/MIC.2019.1901007},

year  = {2019},

date = {2019-01-24},

booktitle = {Proceedings on Minimally Invasive Surgery, Luebeck, Germany},

note = {January 24-25, 2019},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Rottmann2020HiBO,

title = {Learning Hierarchical Acquisition Functions for Bayesian Optimization},

author = {N. Rottmann and T. Kunavar and J. Babič and J. Peters and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/IROS2020Rottmann.pdf, Article File},

year  = {2020},

date = {2020-10-25},

booktitle = {International Conference on Intelligent Robots and Systems (IROS’ 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Xue2020,

title = {Sample-Efficient Covariance Matrix Adaptation Evolutional Strategy via Simulated Rollouts in Neural Networks},

author = {H. Xue and S. Boettger and N. Rottmann and H. Pandya and R. Bruder and G. Neumann and A. Schweikard and E. Rueckert},

url = {https://cps.unileoben.ac.at/wp/ASPAI2020Xue.pdf, Article File},

year  = {2020},

date = {2020-06-30},

booktitle = {International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI’ 2020)},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{dave2025disdp,

title = {DisDP: Robust Imitation Learning via Disentangled Diffusion Policies},

author = {Pankhuri Vanjani and Paul Mattes and Maximilian Xiling Li and Vedant Dave and Rudolf Lioutikov},

url = {https://openreview.net/pdf?id=0GSL9VRBib},

year  = {2025},

date = {2025-05-10},

urldate = {2025-05-10},

booktitle = {Reinforcement Learning Conference (RLC)},

publisher = {Reinforcement Learning Journal},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dave2025bb,

title = {Skill Disentanglement in Reproducing Kernel Hilbert Space},

author = {Vedant Dave and Elmar Rueckert 

},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/m9XKo4t2FXAH6Cs},

doi = {https://doi.org/10.1609/aaai.v39i15.33774},

year  = {2025},

date = {2025-04-11},

urldate = {2025-02-27},

booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)},

volume = {39},

number = {15},

pages = {16153-16162},

abstract = {Unsupervised Skill Discovery aims at learning diverse skills without any extrinsic rewards and leverage them as prior for learning a variety of downstream tasks. Existing approaches to unsupervised reinforcement learning typically involve discovering skills through empowerment-driven techniques or by maximizing entropy to encourage exploration. However, this mutual information objective often results in either static skills that discourage exploration or maximise coverage at the expense of non-discriminable skills. Instead of focusing only on maximizing bounds on f-divergence, we combine it with Integral Probability Metrics to maximize the distance between distributions to promote behavioural diversity and enforce disentanglement. Our method, Hilbert Unsupervised Skill Discovery (HUSD), provides an additional objective that seeks to obtain exploration and separability of state-skill pairs by maximizing the Maximum Mean Discrepancy between the joint distribution of skills and states and the product of their marginals in Reproducing Kernel Hilbert Space. Our results on Unsupervised RL Benchmark show that HUSD outperforms previous exploration algorithms on state-based tasks.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2025,

title = {EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments},

author = {Linus Nwankwo and Bjoern Ellensohn and Vedant Dave and Peter Hofer and Jan Forstner and Marlene Villneuve and Robert Galler and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/MawgtYSbTBoNBZo},

year  = {2025},

date = {2025-01-27},

urldate = {2025-01-27},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2025).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Lygerakis2024,

title = {M2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation},

author = {Fotios Lygerakis and Vedant Dave and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/NPejb2Fp4Y8LeyZ},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dave2024b,

title = {Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training},

author = {Vedant Dave* and Fotios Lygerakis* and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/Nw9TprfdDoLgr8e},

doi = {10.1109/ICRA57147.2024.10610228},

isbn = {979-8-3503-8457-4},

year  = {2024},

date = {2024-01-28},

urldate = {2024-01-28},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},

pages = {8013-8020},

publisher = {IEEE},

abstract = {The rapidly evolving field of robotics necessitates methods that can facilitate the fusion of multiple modalities. Specifically, when it comes to interacting with tangible objects, effectively combining visual and tactile sensory data is key to understanding and navigating the complex dynamics of the physical world, enabling a more nuanced and adaptable response to changing environments. Nevertheless, much of the earlier work in merging these two sensory modalities has relied on supervised methods utilizing datasets labeled by humans. This paper introduces MViTac, a novel methodology that leverages contrastive learning to integrate vision and touch sensations in a self-supervised fashion. By availing both sensory inputs, MViTac leverages intra and inter-modality losses for learning representations, resulting in enhanced material property classification and more adept grasping prediction. Through a series of experiments, we showcase the effectiveness of our method and its superiority over existing state-of-the-art self-supervised and supervised techniques. In evaluating our methodology, we focus on two distinct tasks: material classification and grasping success prediction. Our results indicate that MViTac facilitates the development of improved modality encoders, yielding more robust representations as evidenced by linear probing assessments. https://sites.google.com/view/mvitac/home},

note = {* equal contribution},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Dave2022,

title = {Predicting full-arm grasping motions from anticipated tactile responses},

author = {Vedant Dave and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/WzGSNtc5WRLN3EL},

doi = {10.1109/Humanoids53995.2022.9999743},

isbn = {979-8-3503-0979-9},

year  = {2022},

date = {2022-09-26},

urldate = {2022-09-26},

booktitle = {International Conference on Humanoid Robots (Humanoids)},

pages = {464-471},

publisher = {IEEE},

abstract = {Tactile sensing provides significant information about the state of the environment for performing manipulation tasks. Depending on the physical properties of the object, manipulation tasks can exhibit large variation in their movements. For a grasping task, the movement of the arm and of the end effector varies depending on different points of contact on the object, especially if the object is non-homogeneous in hardness and/or has an uneven geometry. In this paper, we propose Tactile Probabilistic Movement Primitives (TacProMPs), to learn a highly non-linear relationship between the desired tactile responses and the full-arm movement. We solely condition on the tactile responses to infer the complex manipulation skills. We formulate a joint trajectory of full-arm joints with tactile data, leverage the model to condition on the desired tactile response from the non-homogeneous object and infer the full-arm (7-dof panda arm and 19-dof gripper hand) motion. We use a Gaussian Mixture Model of primitives to address the multimodality in demonstrations. We also show that the measurement noise adjustment must be taken into account due to multiple systems working in collaboration.

We validate and show the robustness of the approach through two experiments. First, we consider an object with non-uniform hardness. Grasping from different locations require different motion, and results into different tactile responses. Second, we have an object with homogeneous hardness, but we grasp it with widely varying grasping configurations. Our result shows that TacProMPs can successfully model complex multimodal skills and generalise to new situations.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Leonel2022,

title = {Orientation Probabilistic Movement Primitives on Riemannian Manifolds},

author = {Leonel, Rozo* and Vedant, Dave*},

url = {https://cps.unileoben.ac.at/wp/orientation_probabilistic_move.pdf, Article File},

year  = {2022},

date = {2022-01-11},

urldate = {2022-01-11},

booktitle = {Conference on Robot Learning (CoRL)},

volume = {164},

pages = {11},

abstract = {Learning complex robot motions necessarily demands to have models that are able to encode and retrieve full-pose trajectories when tasks are defined in operational spaces. Probabilistic movement primitives (ProMPs) stand out as a principled approach that models trajectory distributions learned from demonstrations. ProMPs allow for trajectory modulation and blending to achieve better generalization to novel situations. However, when ProMPs are employed in operational space, their original formulation does not directly apply to full-pose movements including rotational trajectories described by quaternions. This paper proposes a Riemannian formulation of ProMPs that enables encoding and retrieving of quaternion trajectories. Our method builds on Riemannian manifold theory,

and exploits multilinear geodesic regression for estimating the ProMPs parameters. This novel approach makes ProMPs a suitable model for learning complex full-pose robot motion patterns. Riemannian ProMPs are tested on toy examples to illustrate their workflow, and on real learning-from-demonstration experiments.},

note = {* equal contribution},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2025,

title = {EnvoDat: A Large-Scale Multisensory Dataset for Robotic Spatial Awareness and Semantic Reasoning in Heterogeneous Environments},

author = {Linus Nwankwo and Bjoern Ellensohn and Vedant Dave and Peter Hofer and Jan Forstner and Marlene Villneuve and Robert Galler and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/MawgtYSbTBoNBZo},

year  = {2025},

date = {2025-01-27},

urldate = {2025-01-27},

booktitle = {IEEE International Conference on Robotics and Automation (ICRA 2025).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2024,

title = {The Conversation is the Command: Interacting with Real-World Autonomous Robots Through Natural Language},

author = {Linus Nwankwo and Elmar Rueckert},

url = {https://doi.org/10.1145/3610978.3640723

https://cloud.cps.unileoben.ac.at/index.php/s/YzJdHWDt9ZdqsZs},

doi = {10.1145/3610978.3640723},

isbn = {9798400703232},

year  = {2024},

date = {2024-01-16},

urldate = {2024-01-16},

booktitle = {HRI '24: Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction.},

pages = {808–812},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

organization = {ACM/IEEE},

series = {HRI '24},

abstract = {In recent years, autonomous agents have surged in real-world environments such as our homes, offices, and public spaces. However, natural human-robot interaction remains a key challenge. In this paper, we introduce an approach that synergistically exploits the capabilities of large language models (LLMs) and multimodal vision-language models (VLMs) to enable humans to interact naturally with autonomous robots through conversational dialogue. We leveraged the LLMs to decode the high-level natural language instructions from humans and abstract them into precise robot actionable commands or queries. Further, we utilised the VLMs to provide a visual and semantic understanding of the robot's task environment. Our results with 99.13% command recognition accuracy and 97.96% commands execution success show that our approach can enhance human-robot interaction in real-world applications. The video demonstrations of this paper can be found at https://osf.io/wzyf6 and the code is available at our GitHub repository.},

howpublished = {ACM/IEEE International Conference on Human-Robot Interaction (HRI ’24 Companion)},

key = {ChatGPT, LLMs, ROS, VLMs, autonomous robots, human-robot interaction, natural language interaction},

note = {Published as late breaking results. Supplementary video: https://cloud.cps.unileoben.ac.at/index.php/s/fRE9XMosWDtJ339 },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Nwankwo2023,

title = {Understanding why SLAM algorithms fail in modern indoor environments},

author = {Linus Nwankwo and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/KdZ2E2np5QEnYfL

},

doi = {https://doi.org/10.1007/978-3-031-32606-6_22},

year  = {2023},

date = {2023-05-27},

urldate = {2023-05-27},

booktitle = {International Conference on Robotics in Alpe-Adria-Danube Region (RAAD). },

volume = {135},

pages = {186 - 194},

publisher = {Cham: Springer Nature Switzerland.},

series = {Mechanisms and Machine Science},

abstract = {Simultaneous localization and mapping (SLAM) algorithms are essential for the autonomous navigation of mobile robots. With the increasing demand for autonomous systems, it is crucial to evaluate and compare the performance of these algorithms in real-world environments. 

In this paper, we provide an evaluation strategy and real-world datasets to test and evaluate SLAM algorithms in complex and challenging indoor environments. Further, we analysed state-of-the-art (SOTA) SLAM algorithms based on various metrics such as absolute trajectory error, scale drift, and map accuracy and consistency. Our results demonstrate that SOTA SLAM algorithms often fail in challenging environments, with dynamic objects, transparent and reflecting surfaces. We also found that successful loop closures had a significant impact on the algorithm’s performance. These findings highlight the need for further research to improve the robustness of the algorithms in real-world scenarios. },

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Feith2024A,

title = {Integrating Human Expertise in Continuous Spaces: A Novel Interactive Bayesian Optimization Framework with Preference Expected Improvement},

author = {Nikolaus Feith and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/6rTWAkoXa3zsJxf},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Feith2024B,

title = {Advancing Interactive Robot Learning: A User Interface Leveraging Mixed Reality and Dual Quaternions},

author = {Nikolaus Feith and Elmar Rueckert},

url = {https://cloud.cps.unileoben.ac.at/index.php/s/qCgzbmCJYSH3F97},

year  = {2024},

date = {2024-04-04},

urldate = {2024-04-04},

booktitle = {IEEE International Conference on Ubiquitous Robots (UR 2024)},

organization = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}